GPFlow: Gaussian Process Uncertainty Quantification
Not sure about which function to call in GPFlow, but the principle is simple. If your $Y$ data was scaled:
$$Y_{scaled} = {(Y - mean(Y)) \over {sd(Y)}}$$
So now if you have any prediction (from the scaled model) $\hat{Y}_{scaled}$, with stand error $\sigma_{\hat {Y}_{scaled}}$, then it is a normal distribution ${\cal N}(\hat {Y}_{scaled}, \sigma^2_{\hat {Y}_{scaled}})$. Then you can very easily rescale it back to the original scale ${\cal N}(\hat {Y}, \sigma^2_{\hat {Y}})$, where:
$$\hat {Y} = \hat {Y}_{scaled}\cdot sd(Y) + mean(Y)$$
$$\sigma_{\hat {Y}} = \sigma_{\hat {Y}_{scaled}} \cdot sd(Y)$$